SA Murphy 1

Intro to Interventions in Digital Health via HeartSteps

Intro to MRT

Above is 20 min with 5 min discussion

some characteristics of digital health in clinical research settings

large no. of stakeholders with differing data needs

need to contribute to behavioral science

low signal to noise ratio with limited data -->intermixing behavioral science with data science

References: (see http://people.seas.harvard.edu/~samurphy/research.html)

Seewald NJ, Smith SN, Lee AJ, Klasnja P, Murphy, S.A. Practical Considerations for Data Collection and Management in Mobile Health Micro-randomized Trials. Stat Biosci. 2019 Jul;11(2):355-370. doi: 10.1007/s12561-018-09228-w. Epub 2019 Jan 5. PubMed PMID: 31462937; PubMed Central PMCID: PMC6713230

Liao, P., Dempsey, W., Sarker, H., Hossain S.M., al’Absi, M., Klasnja, P., and

Murphy, S.A., Just-in-Time But Not Too Much: Determining Treatment Timing in Mobile Health, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Vol. 2, No. 4, Article 179. December 2018. PMCID: PMC6380673.

Walton, A., Nahum‐Shani, I., Crosby, L., Klasnja, P., & Murphy, S. (2018). Optimizing Digital Integrated Care via Micro‐Randomized Trials. Clinical Pharmacology & Therapeutics, 104 (1), 53-58. http://doi.org/10.1002/cpt.1079 NIHMS ID 956009, PMC5995647

Luers, B., Klasnja P. and Murphy, S.A., Standardized effect sizes for preventive mobile health interventions in micro-randomized trials Prev Sci. 2018 Jan 9. doi: 10.1007/s11121-017-0862-5.PMCID: PMC6037616

Smith, S.S., Lee, A.J., Hall, K., Seewald, N.J., Boruvka, A., Murphy, S.A. and Klasnja, P., Design Lessons from a Micro-Randomized Pilot Study in Mobile Health, (2017) Mobile Health Sensors, Analytic Methods, and Applications , Springer International Publishing AG 2017, J.M. Rehg et al. (eds.), DOI 10.1007/978-3-319-51394-2_4, pgs. 59-82.

Nahum-Shani, I., Smith, S.N. Spring, B.J., Collins, L.M., Witkiewitz, K., Tewari, A., & Murphy, S.A.. (2018). Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support. Annals of Behavioral Medicine. May 18;52(6):446-462.doi:10.1007/s12160-016-9830-8, PMCID: PMC5364076

Liao,P., Klasnja, P., Tewari, P., Murphy, S.A. Sample Size Calculations for Micro-Randomized Trials in mHealth, Statistics in Medicine. 2016 May 30;35(12):1944-71. [2015 Dec 28 Epub ahead of print] PubMed PMID: 26707831, PMCID PMC4848174.

Dempsey, W., Liao, P., Klasnja, P., Nahum-Shani, I. Murphy, S.A. (2015). Randomized trials for the Fitbit generation, Significance. 12(6):20-23. PMCID: PMC4721268

Klasnja, P., Hekler, E.B., Shiffman, S., Boruvka, A., Almirall, D., Tewari, A. and Murphy, S.A. (2015). Micro-randomized trials: An experimental design for developing just-in-time adaptive interventions, Health Psychology. Vol 34(Suppl):1220-1228. doi: 10.1037/hea0000305. PubMed PMID: 26651463; PubMed Central PMCID: PMC473257

SA Murphy 3

Outline

Intro to Interventions in Digital Health via HeartSteps

Intro to MRT

Above is 20 min with 5 min discussion

3. Causal Inference & Analysis of Data from an MRT

Above is 45 min with 10 min discussion

5. Designing more interesting MRTs

1. Involving personalization

Above is 45 min with 10 min discussion

6. We planned for the MRT to involve personalization, "precision health." Did it?!

Above is 30 min with 10 min discussion

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Example Intervention components•Behavioral strategies, cognitive strategies, self-monitoring, social linkages, motivational messages, engagement strategies, reminders •Engagement strategies (e.g. to encourage self-monitoring or to encourage receptivity to treatment)•Whether to provide an intervention or whether to prompt self-monitoring•How to deliver an intervention option (via a message on wearable, smartphone notification, SMS)•“Provide nothing” option

Reminders

Suggestions, tips, motivational messages

Prompts to set goals, complete self-report…

Rewards for goal attainment

RecommendationsReach out recommendation (contact a friend)Behavioral strategies (exercise; stay in locations that are supportive of change) Cognitive strategies (relaxation; reframing) Motivational messages (reasons for behavior change; barriers for change); Setting goals; modifying goals Feedback (often with visualization: fish; flower; garden)Distractions (game, music, etc.)

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Can use sensing and user modeling to determine right delivery time

Don’t rely on user’s awareness of times of need or remembering to access

But…

High burden

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Activity suggestions are to help in your automated processing

Evening planning is to help in your reflective processing (controlled behavior)

There are many intervention components that make up a mobile health intervention. We only experiment with a few.

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Only exploration during MRT. No dynamic exploitation of knowledge learned during MRT. To design the MRT we exploit prior knowledge/data but once MRT is designed all learning within the MRT is not exploited to select actions(==options of the intervention components) during the MRT.

Information is exploited/used to enhance efficacy between MRTs not within MRTs --at least in this module!

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Question: How might AI be used with existing data to suggest decision times?

Decision times are times at which researchers believe the action is likely to be effective.

Here the decision times were selected because these times are the times at which most people are able to be activePre-morning commute, mid-day, mid-afternoon, evening commute, after dinner.

Another example: The phone software monitors a risk measure at regular time intervals and if the risk measures hits a criterion then a treatment is provided.

Can include time of day or day of week and present weather. We are most interested in observations that are predictive of the proximal outcome or mightbe used to identify states in which one of the actions is more effective than the other actions.

Question: How might AI be use to construct meaningful, interpretable features of the observations and past observations?

Today we keep things very simple and focus on binary actions—push an activity suggestion vs no push.

The content of the activity suggestion is always tailored based on the individual’s current observations/context/features.

If A_t=1 then another randomization occurs uniformly between low burden and high burden activity suggestions.

Much of Walter’s workshop and later today will concern algorithms for selecting these probabilities.

Here we selected 0.6 because we aimed to provide around 2-3 messages per day per user.

Question: How to use AI + existing data to identify potential mediators? How to use AI to determine the length of time over which the proximal outcome is measured?

Frequently the actions are primarily designed to have a near-term effect on the individual. E.g. Help then manage current craving/stress, help them manage or be aware of the impact of their social setting on their craving/stress

Proximal Outcomes are Mediators that are thought to be critical to achieving a longer term clinical health outcome such as improved heart health1)Short term targeted behavior (step count)Substance use over x hoursPhysical activity over x minutesAdherence over next hour2)Short term risk Current craving, stress 3)Engagement with mobile app/intervention burden

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All decision rules/ causal inferences will be constrained by availability.

In heartsteps, decision point is available for a user if

1. user is not currently potentially operating a car, (unethical to deliver)

2. user is not currently walking, and (not scientific to deliver) another example is available only if currently classified as at risk.

3. user has not turned off intervention (user has agency)

3. user’s phone phone is connected to the internet. (technical concerns) we added this when we realized there was a bug in the software code that prevented intervention delivery when phone was not connected.

Availability is not equivalent to willingness to be treated. It is momentary. Willingness to be treated/enter the study is up front. Our availability is closer to feasibility of trt options

Adherence (i.e. compliance) is very different from availability. Suppose a decision point is available for a user at a decision point. However the phone is in their purse across the room. So they don’t hear whether the phone pings/ see the lockscreenlight up. This person is non-adherent at this decision point. Primary analyses will be intention-to-treat and thus will average over non-compliance.

Context are features/summaries of observations includes

moderators that inform which action (intervention option) is best when and where.

Often past proximal outcomes: stress, activity

Risk, protective, receptivity factors: busyness of calendar, current mood or craving, location, social context, current use of phone

Adherence & burden

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Why not use existing data with non-randomized actions (e.g. observational data)????

Fisher and G. Box in industrial engineering philosophy: “iterative nature of experimentation” when the underlying system is not under control. Always be clear is about what the goal is –what is it you want to optimize.

Data + idea -> deduction (used to test a theory)

Data – idea induction (used to generate new theory)

Can change the objective after you experiment

http://www.statisticsviews.com/details/video/5018561/The-Iterative-Nature-of-Experimentation---Part-II-by-George-E_P_-Box.html

Quote from paper by 4 Google scientists: At Google, experimentation is practically a mantra; we evaluate almost every change that potentially affects what our users experience. Such changes include not only obvious user-visible changes such as modifications to a user interface, but also more subtle changes such as different machine learning algorithms that might affect ranking or content selection. Our insatiable appetite for experimentation has led us to tackle the problems of how to run more experiments, how to run experiments that produce better decisions, and how to run them faster.

Overlapping Experiment Infrastructure: More, Better, Faster Experimentation Diane Tang, Ashish Agarwal, Deirdre O’Brien, Mike Meyer Google, Inc. Mountain View,

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CA [diane,agarwal,deirdre,mmm]@google.com

Quote from paper by 3 facebook scientists and cs at stanford: “Online experiments are widely used to compare specific design

alternatives, but they can also be used to produce generalizable knowledge and inform strategic decision making. Doing so often

requires sophisticated experimental designs, iterative refinement, and careful logging and analysis.”